Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts
Author
Haggenmüller, Sarah
Maron, Roman C.
Hekler, Achim
Utikal, Jochen S.
Barata, Catarina
Barnhill, Raymond L.
Beltraminelli, Helmut
Berking, Carola
Betz-Stablein, Brigid
Blum, Andreas
Braun, Stephan A.
Carr, Richard
Combalia, Marc
Fernandez Figueras, Maria-Teresa
Ferrara, Gerardo
Fraitag, Sylvie
French, Lars E.
Gellrich, Frank F.
Ghoreschi, Kamran
Goebeler, Matthias
Guitera, Pascale
Haenssle, Holger A.
Haferkamp, Sebastian
Heinzerling, Lucie
Heppt, Markus V.
Hilke, Franz J.
Hobelsberger, Sarah
Krahl, Dieter
Kutzner, Heinz
Lallas, Aimilios
Liopyris, Konstantinos
Llamas-Velasco, Mar
Malvehy, Josep
Meier, Friedegund
Müller, Cornelia S.L.
Navarini, Alexander A.
Navarrete-Dechent, Cristián
Perasole, Antonio
Poch, Gabriela
Podlipnik, Sebastian
Requena, Luis
Rotemberg, Veronica M.
Saggini, Andrea
Sangueza, Omar P.
Santonja, Carlos
Schadendorf, Dirk
Schilling, Bastian
Schlaak, Max
Schlager, Justin G.
Sergon, Mildred
Sondermann, Wiebke
Soyer, H. Peter
Starz, Hans
Stolz, Wilhelm
Vale, Esmeralda
Weyers, Wolfgang
Zink, Alexander
Krieghoff-Henning, Eva I.
Kather, Jakob N.
Von Kalle, Christof
Lipka, Daniel B.
Fröhling, Stefan
Hauschild, Axel
Kittler, Harald
Brinker, Titus J.
Publication date
2021-10ISSN
0959-8049
Abstract
Background: Multiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians. Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included. Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images. Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
61 - Medical sciences
616.5 - Skin. Common integument. Clinical dermatology. Cutaneous complaints
Keywords
Classificació del càncer de pell
Biomarcadors
Biomarcadors digitals
Càncer de pell
Xarxa neuronal de convolució
Intel·ligència artificial
Aprenentatge automàtic
Aprenentatge profund
Dermatologia
Melanoma maligne
Clasificación del cáncer de piel
Biomarcadores
Biomarcadores digitales
Cáncer de piel
Red neuronal de convolución
Inteligencia artificial
Aprendizaje automático
Aprendizaje profundo
Dermatología
Melanoma maligno
Classification of skin cancer
Biomarkers
Digital biomarkers
Skin cancer
Neural network of convolution
Artificial intelligence
Machine learning
Deep learning
Dermatology
Malignant melanoma
Pages
15
Publisher
Elsevier
Collection
156;
Is part of
European Journal of Cancer
Citation
Haggenmüller, Sarah; Maron, Roman C.; Hekler, Achim [et al.]. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. European Journal of Cancer, 2021, 156, p. 202-216. Disponible en: <https://www.sciencedirect.com/science/article/pii/S0959804921004445?via%3Dihub>. Fecha de acceso: 19 oct. 2021. DOI: 10.1016/j.ejca.2021.06.049
This item appears in the following Collection(s)
- Ciències de la Salut [745]
Rights
2021 - The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/